Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Danish
bert
feature-extraction
text-embeddings-inference
Instructions to use KennethTM/MiniLM-L6-danish-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use KennethTM/MiniLM-L6-danish-encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KennethTM/MiniLM-L6-danish-encoder") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
| { | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "mask_token": "[MASK]", | |
| "max_length": 128, | |
| "model_max_length": 512, | |
| "pad_to_multiple_of": null, | |
| "pad_token": "[PAD]", | |
| "pad_token_type_id": 0, | |
| "padding_side": "right", | |
| "sep_token": "[SEP]", | |
| "stride": 0, | |
| "tokenizer_class": "PreTrainedTokenizerFast", | |
| "truncation_side": "right", | |
| "truncation_strategy": "longest_first", | |
| "unk_token": "[UNK]", | |
| "vocab": "bert-uncased-tokenizer-danish/vocab.txt" | |
| } | |